Salary, job growth, and location sit beside the AI-exposure number so you can weigh a path properly. They are display only, and by design they cannot shift the exposure figure by a single point.
Sample data. Every figure below is illustrative, not a real wage estimate.
Median pay where you would actually work
A national median hides two things that change the number a lot: the metro you are in, and whether the role sits in an office.
Cambridge, Massachusetts
🔒Fixed to the sample ZIP code. Entering your own turns on when the wage engine is connected at launch.
The exposure number on this page is built from O*NET task data, Eloundou et al., and METR, and from nothing else. Wages, growth projections, your ZIP code, your network, and your portfolio are all shown beside it and none of them can move it. That separation is the reason the number means anything. A 0–100 exposure scale.
This projection uses METR's established long-run time-horizon trend, not the faster 2026 estimates. Those newer figures come from a task suite near saturation, carry very wide confidence intervals, and measure software tasks specifically, so extrapolating them across all occupations is not yet reliable. See MIT Technology Review, "This is the most misunderstood graph in AI," February 5, 2026, technologyreview.com
The published AI-exposure number is built only from O*NET task data, Eloundou et al., and METR. Pay, growth, networking, portfolio, and clean-up are context and never move it. A 0–100 exposure scale.
This dashboard is a sample. Maya Rivera, Northlight University, Northbeam, Lumen, and Verdi Labs are invented, and every figure on this page is sample data, not a real result.
